QoE-driven Scheme Of Cooperative Optimization Of Wireless Video Storage And Transmission

With the development of 5G,there is a growing demand for video services.But The growth of wireless network resources cannot let all users obtain high-quality video experience.So,more and more researchers investigate wireless video optimization technologies to improve the quality of the user experience.Transmission and storage are the two main concerns.Good transmission scheme can adapt to the unstable wireless network environment,and can make full use of wireless network resources.As for the storage,it saves the network routing time,and directly caches the popular videos and the appropriate video bit rate versions on the access network side,which saves the server's computation and transcoding consumption.It then further improves the user experience.In this paper,we study the video transmission and the video cache.The main contents are as follows:Firstly,we research and analyze the mainstream of the rate adaptive schemes,which are mainly divided into:the rate of adaptation based on bandwidth estimation scheme,the rate of adaptation based on the buffer rate of the adaptive scheme,and the model based on the rate of adaptive control scheme.And we analysis the strength and weakness of various algorithms.Secondly,we use the Zipf model to model the relationship between the video ranking and the probability of the request.Then,we use the linear regression model to model the relationship between QoE(Quality of Experience),request rate,response rate and bandwidth.Then we use the convex optimization method to solve the problem,which obtains the number of video versions and the rate video for each version to make maximum QoE.According to the simulations,our caching scheme can improve the quality of user experience by 1.6.